Preparar el Entorno¶

In [1]:
# pip install --upgrade pycaret scipy
In [2]:
# Importación de librerías esenciales para manipulación y análisis de datos
import pandas as pd
import numpy as np

# Importación de librerías para visualización de datos
import matplotlib.pyplot as plt
import seaborn as sns

# Herramientas de preprocesamiento de sklearn
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder

# Modelos y herramientas de selección y evaluación de sklearn
from sklearn.feature_selection import f_classif, SelectKBest
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LassoCV
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, BaggingClassifier
from sklearn.impute import KNNImputer
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, median_absolute_error, explained_variance_score
from sklearn.pipeline import Pipeline

# Configuraciones específicas para mejorar la visualización en cuadernos Jupyter
pd.set_option('display.max_rows', 50)  # Limitar el máximo de filas visibles para mejorar la performance
pd.set_option('display.max_columns', None)  # Mostrar todas las columnas disponibles

# Ajustes del sistema y manejo de módulos personalizados
import sys
sys.path.append('../src/utils')

# Importación de módulos personalizados para funcionalidades específicas
from utils import (
    load_data,
    plot_boxplot,
    plot_category_counts,
    plot_heatmap
)

from preprocess import *
from modeling_dev import *

# Importaciones específicas de PyCaret para facilitar el modelado y la clasificación
from pycaret.classification import *
from pycaret.utils import version
version()  # Muestra la versión de PyCaret para asegurar compatibilidad
Out[2]:
'3.3.2'

Cargar los Datos¶

In [3]:
df = load_data('../data/processed/prediction_dataset_complete.csv')
print("Dimensiones del DataFrame cargado:", df.shape)

Datos cargados con dimensiones iniciales: (717, 66)
Dimensiones del DataFrame cargado: (717, 66)
In [4]:
df.head()
Out[4]:
edad sexo altura peso num calzado articulacion localizacion lado pace_walk velocidad_walk step rate_walk stride length_walk shock_walk impact gs_walk braking gs_walk footstrike type_walk pronation excursion_walk contact ratio_walk total force rate_walk step length_walk pronation excursion (mp->to)_walk stance excursion (fs->mp)_walk stance excursion (mp->to)_walk m1 hipermovil thomas psoas thomas rf thomas tfl ober arco aplanado arco elevado m1 dfx m5 hipermovil arco transverso disminuido m1 pfx arco transverso aumentado hlf hl hr hav index minus tfi tfe tti tte ober friccion popliteo t_hintermann jack normal jack no reconstruye pronacion no disponible 2heel raise heel raise fpi_total_i fpi_total_d tibia vara proximal tibia vara distal rotula divergente rotula convergente rotula ascendida genu valgo genu varo genu recurvatum genu flexum lunge imc zona afectada
0 36 0 173 65.0 40.0 rodilla medial b 1.291600 5.0 108.456898 1.4267 3.566339 2.310324 2.610172 6.314815 -11.106498 63.724990 24.952120 0.7133 -14.725130 19.445907 62.315404 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 3 3 0 3 0 3 0 0 1 3 0 0 0 0 -1.0 -1.0 0 0 0 0 0 0 0 3 0 1 21.72 rodilla_medial_b
1 35 1 181 78.0 44.0 sin afectacion no especificado no especificado 1.370079 5.0 114.767847 1.4312 3.119538 1.900867 2.387259 6.948276 -8.176466 62.112399 30.589598 0.7156 0.962024 17.340533 55.030184 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7.0 7.0 0 0 0 0 0 0 0 0 0 0 23.81 sin patologia
2 45 1 189 88.0 46.0 sin afectacion no especificado no especificado 1.371909 5.0 100.149777 1.6437 2.368494 1.597828 1.558757 10.680851 -4.411249 64.721319 32.726926 0.8218 0.192496 8.006323 77.799223 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6.0 5.0 0 0 0 0 0 0 0 0 0 0 24.64 sin patologia
3 43 1 182 70.0 44.0 pie medial d 1.318286 5.0 103.976334 1.5381 2.667928 1.708754 1.985392 11.180000 -4.351264 63.545858 28.220870 0.7690 6.506884 7.471882 81.783758 3 0 3 0 0 0 0 3 0 0 0 0 0 0 0 0 3 0 0 0 3 0 0 0 3 0 0 0 0 7.0 7.0 0 0 0 0 0 0 0 3 0 0 21.13 pie-tobillo_medial_d
4 41 1 184 90.0 43.0 pierna posterior b 1.362836 5.0 99.062660 1.6541 3.610964 2.665554 2.369300 7.900000 -10.180634 67.074954 33.841236 0.8270 -6.480006 14.944068 89.849386 0 0 3 1 0 0 3 0 0 0 3 3 0 0 0 0 3 0 0 0 3 0 3 0 0 0 0 0 0 3.0 0.0 1 0 0 0 0 0 3 0 0 3 26.58 pierna_posterior_b
In [5]:
print(f"Nº de categorías únicas en 'articulacion' antes de la transformación: {df['articulacion'].nunique()}")

Nº de categorías únicas en 'articulacion' antes de la transformación: 8
In [6]:
# Agrupación de categorías menos frecuentes en una nueva categoría llamada 'otro'
df['articulacion'] = df['articulacion'].replace(['pierna', 'cadera', 'espalda', 'sin afectacion', 'muslo', 'complejo'], 'otro')
print(f"Número de categorías únicas en 'articulacion' después de la transformación: {df['articulacion'].nunique()}")

Número de categorías únicas en 'articulacion' después de la transformación: 4
In [7]:
# Eliminación de columnas que no se utilizarán
df = df.drop(columns=['localizacion', 'lado', 'zona afectada'])
In [8]:
plot_boxplot(df) # Visualización

División en train y test¶

In [9]:
# Preparación de los datos
X = df.drop('articulacion', axis=1)  # Eliminar la columna objetivo del conjunto de características
y = df['articulacion']               # Definir el conjunto objetivo

# División del conjunto de datos en entrenamiento y test
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Visualización de las dimensiones de los conjuntos de datos
print(f'Dimensiones del dataset de train: {X_train.shape} | {y_train.shape}')
print(f'Dimensiones del dataset de validación: {X_test.shape} | {y_test.shape}')

X_train.head()

Dimensiones del dataset de train: (573, 62) | (573,)
Dimensiones del dataset de validación: (144, 62) | (144,)
Out[9]:
edad sexo altura peso num calzado pace_walk velocidad_walk step rate_walk stride length_walk shock_walk impact gs_walk braking gs_walk footstrike type_walk pronation excursion_walk contact ratio_walk total force rate_walk step length_walk pronation excursion (mp->to)_walk stance excursion (fs->mp)_walk stance excursion (mp->to)_walk m1 hipermovil thomas psoas thomas rf thomas tfl ober arco aplanado arco elevado m1 dfx m5 hipermovil arco transverso disminuido m1 pfx arco transverso aumentado hlf hl hr hav index minus tfi tfe tti tte ober friccion popliteo t_hintermann jack normal jack no reconstruye pronacion no disponible 2heel raise heel raise fpi_total_i fpi_total_d tibia vara proximal tibia vara distal rotula divergente rotula convergente rotula ascendida genu valgo genu varo genu recurvatum genu flexum lunge imc
75 45 1 178 80.0 44.1 1.431927 5.0 112.891509 1.5229 2.326613 2.014790 1.131385 12.089286 -1.934450 63.539849 29.526575 0.7614 -12.226850 5.191136 80.358554 0 0 3 3 0 3 0 0 0 0 0 0 0 0 0 0 3 0 3 0 0 0 3 0 3 0 0 0 0 4.0 5.0 3 0 0 0 0 0 0 3 0 3 25.25
256 43 1 170 71.0 42.0 1.316527 5.0 113.289981 1.4059 1.859483 0.987038 1.464532 11.022472 -3.463675 64.150504 28.362304 0.7030 -8.461037 7.424516 74.429788 0 2 3 1 0 0 0 0 0 0 3 0 0 0 0 0 3 0 3 0 0 1 3 0 3 0 0 0 0 -1.0 -1.0 3 3 0 0 0 0 3 0 0 0 24.57
626 30 1 178 74.0 41.0 1.383853 5.0 108.546666 1.5321 1.973759 1.317979 1.445499 9.339623 -9.473472 64.472397 16.185594 0.7661 -2.814574 11.068998 79.124419 0 0 3 0 0 0 3 0 0 0 0 0 0 0 0 0 3 0 3 0 3 0 3 0 3 0 0 0 0 -4.0 -6.0 3 0 3 0 0 0 0 0 0 3 23.36
8 50 0 165 67.0 37.0 0.083356 5.0 124.494250 0.0803 2.660461 1.821702 1.860127 11.419355 -6.135594 66.482163 16.611606 0.0401 -1.784790 6.559235 61.928437 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 3 3 0 0 0 3 0 0 0 3 0 0 0 0 9.0 9.0 3 3 0 0 0 0 0 0 0 3 24.61
149 47 0 170 60.0 40.0 1.381579 5.0 112.642498 1.4708 2.907667 2.121389 1.946341 12.375000 -4.351580 62.359526 28.840264 0.7354 -7.837623 4.836789 79.862173 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 3 0 0 0 0 5.0 1.0 0 2 0 0 0 3 0 3 0 0 20.76
In [10]:
# Guardamos
# X_train.to_csv('../data/processed/sin_escalar/X_train.csv', sep=';', decimal='.', index=False)
# X_test.to_csv('../data/processed/sin_escalar/X_test.csv', sep=';', decimal='.', index=False)
# y_train.to_csv('../data/processed/sin_escalar/y_train.csv', sep=';', decimal='.', index=False)
# y_test.to_csv('../data/processed/sin_escalar/y_test.csv', sep=';', decimal='.', index=False)
In [11]:
# Cargar los dataset
# X_train = pd.read_csv('../data/processed/sin_escalar/X_train.csv', sep=';', decimal='.')
# X_test = pd.read_csv('../data/processed/sin_escalar/X_test.csv', sep=';', decimal='.')
# y_train = pd.read_csv('../data/processed/sin_escalar/y_train.csv', sep=';', decimal='.')
# y_test = pd.read_csv('../data/processed/sin_escalar/y_test.csv', sep=';', decimal='.')

X_train.head()
Out[11]:
edad sexo altura peso num calzado pace_walk velocidad_walk step rate_walk stride length_walk shock_walk impact gs_walk braking gs_walk footstrike type_walk pronation excursion_walk contact ratio_walk total force rate_walk step length_walk pronation excursion (mp->to)_walk stance excursion (fs->mp)_walk stance excursion (mp->to)_walk m1 hipermovil thomas psoas thomas rf thomas tfl ober arco aplanado arco elevado m1 dfx m5 hipermovil arco transverso disminuido m1 pfx arco transverso aumentado hlf hl hr hav index minus tfi tfe tti tte ober friccion popliteo t_hintermann jack normal jack no reconstruye pronacion no disponible 2heel raise heel raise fpi_total_i fpi_total_d tibia vara proximal tibia vara distal rotula divergente rotula convergente rotula ascendida genu valgo genu varo genu recurvatum genu flexum lunge imc
75 45 1 178 80.0 44.1 1.431927 5.0 112.891509 1.5229 2.326613 2.014790 1.131385 12.089286 -1.934450 63.539849 29.526575 0.7614 -12.226850 5.191136 80.358554 0 0 3 3 0 3 0 0 0 0 0 0 0 0 0 0 3 0 3 0 0 0 3 0 3 0 0 0 0 4.0 5.0 3 0 0 0 0 0 0 3 0 3 25.25
256 43 1 170 71.0 42.0 1.316527 5.0 113.289981 1.4059 1.859483 0.987038 1.464532 11.022472 -3.463675 64.150504 28.362304 0.7030 -8.461037 7.424516 74.429788 0 2 3 1 0 0 0 0 0 0 3 0 0 0 0 0 3 0 3 0 0 1 3 0 3 0 0 0 0 -1.0 -1.0 3 3 0 0 0 0 3 0 0 0 24.57
626 30 1 178 74.0 41.0 1.383853 5.0 108.546666 1.5321 1.973759 1.317979 1.445499 9.339623 -9.473472 64.472397 16.185594 0.7661 -2.814574 11.068998 79.124419 0 0 3 0 0 0 3 0 0 0 0 0 0 0 0 0 3 0 3 0 3 0 3 0 3 0 0 0 0 -4.0 -6.0 3 0 3 0 0 0 0 0 0 3 23.36
8 50 0 165 67.0 37.0 0.083356 5.0 124.494250 0.0803 2.660461 1.821702 1.860127 11.419355 -6.135594 66.482163 16.611606 0.0401 -1.784790 6.559235 61.928437 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 3 3 0 0 0 3 0 0 0 3 0 0 0 0 9.0 9.0 3 3 0 0 0 0 0 0 0 3 24.61
149 47 0 170 60.0 40.0 1.381579 5.0 112.642498 1.4708 2.907667 2.121389 1.946341 12.375000 -4.351580 62.359526 28.840264 0.7354 -7.837623 4.836789 79.862173 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 3 0 0 0 0 5.0 1.0 0 2 0 0 0 3 0 3 0 0 20.76
In [12]:
# Histograma del conjunto de entrenamiento
X_train.hist(bins=20, figsize=(26, 24), color='royalblue')
plt.show()
In [13]:
# Estadísticas descriptivas del conjunto de entrenamiento
display(X_train.describe().round(3))
edad sexo altura peso num calzado pace_walk velocidad_walk step rate_walk stride length_walk shock_walk impact gs_walk braking gs_walk footstrike type_walk pronation excursion_walk contact ratio_walk total force rate_walk step length_walk pronation excursion (mp->to)_walk stance excursion (fs->mp)_walk stance excursion (mp->to)_walk m1 hipermovil thomas psoas thomas rf thomas tfl ober arco aplanado arco elevado m1 dfx m5 hipermovil arco transverso disminuido m1 pfx arco transverso aumentado hlf hl hr hav index minus tfi tfe tti tte ober friccion popliteo t_hintermann jack normal jack no reconstruye pronacion no disponible 2heel raise heel raise fpi_total_i fpi_total_d tibia vara proximal tibia vara distal rotula divergente rotula convergente rotula ascendida genu valgo genu varo genu recurvatum genu flexum lunge imc
count 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.000 573.00 573.000 573.000 573.000
mean 39.215 0.667 173.328 71.631 41.719 1.347 4.975 111.153 1.456 2.315 1.546 1.631 8.301 -7.295 63.490 21.937 0.773 -3.521 14.615 69.521 0.635 0.442 0.916 0.696 0.070 0.658 0.707 0.527 0.079 0.037 0.949 0.272 0.127 0.653 0.073 0.635 2.825 0.445 0.911 0.052 0.712 0.026 0.805 0.133 1.965 0.291 0.091 0.031 0.012 3.330 3.176 0.749 0.576 0.131 0.253 0.021 0.773 0.649 0.55 0.304 0.935 23.736
std 13.544 0.472 8.911 13.583 2.625 0.154 0.155 7.158 0.160 0.875 0.671 0.638 2.613 5.026 1.925 7.771 0.542 7.341 6.712 9.805 1.207 1.022 1.347 1.155 0.449 1.223 1.267 1.108 0.472 0.296 1.380 0.846 0.557 1.154 0.440 1.202 0.698 0.991 1.299 0.380 1.244 0.260 1.302 0.585 1.348 0.805 0.500 0.263 0.150 4.255 4.332 1.264 1.140 0.587 0.811 0.250 1.307 1.225 1.12 0.676 1.301 3.502
min 15.000 0.000 148.000 39.000 36.000 0.083 4.000 85.079 0.080 0.750 0.445 0.587 1.250 -24.901 41.133 5.952 0.040 -22.855 -0.159 32.312 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -11.000 -8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 0.000 0.000 15.040
25% 29.000 0.000 168.000 62.000 40.000 1.361 5.000 106.987 1.412 1.746 1.132 1.209 6.173 -9.849 62.619 14.845 0.706 -8.441 9.161 62.430 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 0.000 0.000 21.260
50% 39.000 1.000 173.000 71.000 42.000 1.371 5.000 110.876 1.477 2.083 1.364 1.496 8.640 -6.237 63.627 19.978 0.739 -3.532 13.729 70.310 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3.000 0.000 0.000 0.000 0.000 3.000 3.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 0.000 0.000 23.410
75% 48.000 1.000 180.000 80.000 44.000 1.379 5.000 114.822 1.531 2.655 1.786 1.866 10.358 -3.663 64.425 27.978 0.767 1.047 19.983 76.695 0.000 0.000 3.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 3.000 0.000 0.000 1.000 0.000 0.000 3.000 0.000 3.000 0.000 1.000 0.000 3.000 0.000 3.000 0.000 0.000 0.000 0.000 6.000 6.000 1.000 0.000 0.000 0.000 0.000 3.000 0.000 0.00 0.000 3.000 25.610
max 80.000 1.000 197.000 135.000 49.000 2.794 5.000 165.523 2.025 8.329 6.403 5.236 15.404 22.671 71.494 52.165 7.664 19.832 37.078 92.868 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 12.000 12.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.00 3.000 3.000 41.670
In [14]:
# Cálculo de percentiles críticos para características específicas
percentil_98_pace = df['pace_walk'].quantile(0.98)
percentil_98_stride_length = df['stride length_walk'].quantile(0.98)

# Filtrado de registros que superen el percentil 98 en características específicas
high_pace_walk = df[df['pace_walk'] > percentil_98_pace]
high_stride_length_walk = df[df['stride length_walk'] > percentil_98_stride_length]

# Visualización de los resultados filtrados
print("Registros con pace_walk alto:")
print(high_pace_walk[['pace_walk', 'stride length_walk']])
print("\nRegistros con stride length_walk alto:")
print(high_stride_length_walk[['pace_walk', 'stride length_walk']])

Registros con pace_walk alto:
     pace_walk  stride length_walk
24    1.466770            1.697700
108   1.460815            1.694800
127   1.495747            1.471800
128   1.495747            1.471800
135   1.492680            1.574900
248   1.482486            1.357700
281   1.467390            1.629000
394   1.477679            1.566000
403   1.455382            1.608100
424   1.486504            1.610200
554   2.325577            1.941543
571   1.907261            1.727432
572   1.907261            1.727432
574   2.793561            2.024550
676   1.454388            1.672300

Registros con stride length_walk alto:
     pace_walk  stride length_walk
24    1.466770            1.697700
108   1.460815            1.694800
220   1.368494            1.717100
239   1.404306            1.688000
240   1.404306            1.688000
241   1.404306            1.688000
343   1.373040            1.811300
344   1.373040            1.811300
552   1.349389            1.764691
554   2.325577            1.941543
571   1.907261            1.727432
572   1.907261            1.727432
574   2.793561            2.024550
628   1.373276            1.673800
674   1.392411            1.748000

Optamos por conservar los registros debido a que parecen legítimos y representativos de un subgrupo específico.

Codificar Variable Objetivo¶

In [15]:
encoder = LabelEncoder()
y_train_encoded = encoder.fit_transform(y_train)
y_test_encoded = encoder.transform(y_test)

Escalado de Datos¶

Para los modelos Random Forest y Decision Tree, que no son sensibles al escalado de las características, ya que realizan divisiones basadas en el orden y no en la magnitud, no escalaremos los datos.

Selección de Características¶

In [16]:
plot_category_counts(y_train, 'Distribución de Categorías en el Dataset')
In [17]:
# Eliminar características constantes
X_train_var = X_train.loc[:, X_train.var() != 0] # En X_train es la columna 'pnca ap valgo'

# Suponiendo que X_train es tu conjunto de entrenamiento y y_train son las etiquetas
selector = SelectKBest(score_func=f_classif, k='all')  # 'all' para seleccionar todas las características y ver sus puntuaciones
selector.fit(X_train_var, y_train)

# Muestra las puntuaciones de ANOVA para cada característica
print("Puntuaciones F de ANOVA:", selector.scores_)

Puntuaciones F de ANOVA: [1.06646085 4.81089112 2.94041921 1.82980159 2.82588555 0.86354468
 0.04679907 1.44185914 1.717855   3.10212622 3.28431398 2.21912517
 0.21422796 1.65781819 1.59975547 8.1668386  0.47655017 0.28126439
 0.09928221 1.70063061 0.26507218 4.39322399 1.94683702 4.76807426
 1.57051599 0.72608804 0.7131213  2.30370136 0.46115443 2.55732254
 0.54754538 0.12844644 1.74342114 1.18480545 1.35850443 2.46706741
 5.8887358  1.28743635 1.00568414 1.38860961 0.51157749 1.56433242
 0.41815726 0.77808872 2.14462007 2.96618735 2.34866709 0.51719317
 0.54210066 0.86955965 0.45150191 2.27720956 0.7994806  1.46988581
 0.71711985 0.04800951 0.87754986 0.11467424 1.58977147 0.17002249
 0.1300058  1.3980537 ]

Las características con las puntuaciones F más altas son más relevantes para predecir la variable objetivo. Por ejemplo, los valores mayores (como 8.20979186 para total force rate_walk) indican una mayor relevancia.

In [18]:
X_train_with_target = X_train.copy()
X_train_with_target['Target'] = y_train_encoded
corr_matrix = X_train_with_target.corr()
plot_heatmap(corr_matrix, figsize=(18, 14), cmap="GnBu", title='', annot=False)
In [19]:
threshold = 0.5 # Umbral de correlación

# Encuentra pares con una correlación superior al umbral
strong_pairs = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
strong_pairs = strong_pairs.stack().reset_index()
strong_pairs.columns = ['variable1', 'variable2', 'correlation']

strong_pairs = strong_pairs[strong_pairs['correlation'] > threshold]  # Filtrar pares con correlación fuerte
strong_pairs = strong_pairs.sort_values('correlation', ascending=False) # Ordenar por correlación
strong_pairs
Out[19]:
variable1 variable2 correlation
522 shock_walk impact gs_walk 0.927667
523 shock_walk braking gs_walk 0.918198
1862 fpi_total_i fpi_total_d 0.902056
124 altura num calzado 0.843144
240 peso imc 0.839313
302 pace_walk stride length_walk 0.819646
64 sexo num calzado 0.771023
575 impact gs_walk braking gs_walk 0.708804
183 peso num calzado 0.683861
62 sexo altura 0.682605
123 altura peso 0.636667
63 sexo peso 0.558533
684 footstrike type_walk stance excursion (mp->to)_walk 0.555707
678 footstrike type_walk pronation excursion_walk 0.555232

Las altas correlaciones entre características (como shock_walk y impact gs_walk) sugieren redundancia. Podemos considerar eliminar una de las características correlacionadas para reducir la multicolinealidad.

PyCaret¶

In [20]:
# Configuración de PyCaret
clf1 = setup(data=df, 
             target='articulacion', 
             session_id=123, 
             log_experiment=True, 
             experiment_name='clasificacion_articulacion',
             preprocess=True,  # Preprocesamiento automático
             train_size=0.8)  # Cómo dividir el conjunto de datos en PyCaret

# Comparar modelos automáticamente
best_model = compare_models()
best_model
  Description Value
0 Session id 123
1 Target articulacion
2 Target type Multiclass
3 Target mapping otro: 0, pie: 1, rodilla: 2, tobillo: 3
4 Original data shape (717, 63)
5 Transformed data shape (717, 63)
6 Transformed train set shape (573, 63)
7 Transformed test set shape (144, 63)
8 Numeric features 62
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Fold Generator StratifiedKFold
14 Fold Number 10
15 CPU Jobs -1
16 Use GPU False
17 Log Experiment MlflowLogger
18 Experiment Name clasificacion_articulacion
19 USI 40bb
  Model Accuracy AUC Recall Prec. F1 Kappa MCC TT (Sec)
gbc Gradient Boosting Classifier 0.4102 0.0000 0.4102 0.4110 0.4033 0.2009 0.2028 0.0820
rf Random Forest Classifier 0.4083 0.6258 0.4083 0.4099 0.3934 0.1938 0.1981 0.0200
et Extra Trees Classifier 0.3979 0.6301 0.3979 0.4042 0.3826 0.1788 0.1824 0.0170
lightgbm Light Gradient Boosting Machine 0.3806 0.6070 0.3806 0.3851 0.3760 0.1592 0.1607 1.3610
xgboost Extreme Gradient Boosting 0.3770 0.6174 0.3770 0.3807 0.3732 0.1564 0.1577 0.0280
dt Decision Tree Classifier 0.3718 0.5775 0.3718 0.3807 0.3705 0.1540 0.1556 0.0880
ridge Ridge Classifier 0.3682 0.0000 0.3682 0.3612 0.3531 0.1403 0.1437 0.0040
lr Logistic Regression 0.3543 0.0000 0.3543 0.3483 0.3454 0.1241 0.1257 0.1500
lda Linear Discriminant Analysis 0.3508 0.0000 0.3508 0.3518 0.3446 0.1214 0.1232 0.0040
ada Ada Boost Classifier 0.3211 0.0000 0.3211 0.3130 0.3119 0.0843 0.0851 0.0100
knn K Neighbors Classifier 0.3018 0.5526 0.3018 0.2882 0.2876 0.0510 0.0519 0.0930
dummy Dummy Classifier 0.2827 0.5000 0.2827 0.0799 0.1246 0.0000 0.0000 0.0050
qda Quadratic Discriminant Analysis 0.2775 0.0000 0.2775 0.2796 0.2038 0.0120 0.0179 0.0040
svm SVM - Linear Kernel 0.2758 0.0000 0.2758 0.2105 0.1817 0.0186 0.0215 0.0070
nb Naive Bayes 0.2601 0.5317 0.2601 0.2199 0.1880 0.0074 0.0070 0.0860
Out[20]:
GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
                           learning_rate=0.1, loss='log_loss', max_depth=3,
                           max_features=None, max_leaf_nodes=None,
                           min_impurity_decrease=0.0, min_samples_leaf=1,
                           min_samples_split=2, min_weight_fraction_leaf=0.0,
                           n_estimators=100, n_iter_no_change=None,
                           random_state=123, subsample=1.0, tol=0.0001,
                           validation_fraction=0.1, verbose=0,
                           warm_start=False)
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GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
                           learning_rate=0.1, loss='log_loss', max_depth=3,
                           max_features=None, max_leaf_nodes=None,
                           min_impurity_decrease=0.0, min_samples_leaf=1,
                           min_samples_split=2, min_weight_fraction_leaf=0.0,
                           n_estimators=100, n_iter_no_change=None,
                           random_state=123, subsample=1.0, tol=0.0001,
                           validation_fraction=0.1, verbose=0,
                           warm_start=False)
In [21]:
rf = create_model('rf', fold=10)
  Accuracy AUC Recall Prec. F1 Kappa MCC
Fold              
0 0.3793 0.5943 0.3793 0.3814 0.3590 0.1529 0.1578
1 0.4655 0.6127 0.4655 0.4444 0.4386 0.2709 0.2761
2 0.3966 0.6076 0.3966 0.4240 0.3797 0.1751 0.1802
3 0.4386 0.6480 0.4386 0.4582 0.4167 0.2294 0.2370
4 0.4211 0.6725 0.4211 0.4087 0.4094 0.2133 0.2150
5 0.3860 0.6101 0.3860 0.3960 0.3739 0.1701 0.1746
6 0.4035 0.6463 0.4035 0.4109 0.3995 0.1908 0.1934
7 0.3684 0.6373 0.3684 0.3191 0.3393 0.1334 0.1363
8 0.4211 0.6073 0.4211 0.4145 0.4132 0.2169 0.2185
9 0.4035 0.6220 0.4035 0.4422 0.4043 0.1847 0.1919
Mean 0.4083 0.6258 0.4083 0.4099 0.3934 0.1938 0.1981
Std 0.0276 0.0231 0.0276 0.0375 0.0284 0.0380 0.0383
In [22]:
plot_model(rf)
In [23]:
plot_model(rf, plot= 'confusion_matrix')
In [24]:
plot_model(rf, plot= 'feature')
In [25]:
plot_model(rf, plot= 'pr')
In [26]:
plot_model(rf, plot='class_report')
In [27]:
et = create_model('et', fold=10)
  Accuracy AUC Recall Prec. F1 Kappa MCC
Fold              
0 0.4138 0.5873 0.4138 0.4463 0.4099 0.1971 0.2002
1 0.3793 0.6156 0.3793 0.3983 0.3593 0.1519 0.1554
2 0.4310 0.6341 0.4310 0.3624 0.3924 0.2242 0.2300
3 0.3860 0.6569 0.3860 0.4959 0.3692 0.1575 0.1641
4 0.3860 0.6533 0.3860 0.3688 0.3710 0.1635 0.1653
5 0.4737 0.6741 0.4737 0.4890 0.4669 0.2899 0.2963
6 0.4035 0.6449 0.4035 0.3900 0.3866 0.1843 0.1878
7 0.4035 0.6361 0.4035 0.3958 0.3866 0.1826 0.1855
8 0.3333 0.6004 0.3333 0.3318 0.3320 0.1016 0.1017
9 0.3684 0.5983 0.3684 0.3636 0.3523 0.1349 0.1372
Mean 0.3979 0.6301 0.3979 0.4042 0.3826 0.1788 0.1824
Std 0.0358 0.0272 0.0358 0.0525 0.0351 0.0491 0.0505
In [28]:
plot_model(et)
In [29]:
plot_model(et, plot= 'confusion_matrix')
In [30]:
plot_model(et, plot= 'feature')

Selección de Características¶

In [31]:
df.columns
Out[31]:
Index(['edad', 'sexo', 'altura', 'peso', 'num calzado', 'articulacion',
       'pace_walk', 'velocidad_walk', 'step rate_walk', 'stride length_walk',
       'shock_walk', 'impact gs_walk', 'braking gs_walk',
       'footstrike type_walk', 'pronation excursion_walk',
       'contact ratio_walk', 'total force rate_walk', 'step length_walk',
       'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk',
       'stance excursion (mp->to)_walk', 'm1 hipermovil', 'thomas psoas',
       'thomas rf', 'thomas tfl', 'ober', 'arco aplanado', 'arco elevado',
       'm1 dfx', 'm5 hipermovil', 'arco transverso disminuido', 'm1 pfx',
       'arco transverso aumentado', 'hlf', 'hl', 'hr', 'hav', 'index minus',
       'tfi', 'tfe', 'tti', 'tte', 'ober friccion', 'popliteo', 't_hintermann',
       'jack normal', 'jack no reconstruye', 'pronacion no disponible',
       '2heel raise', 'heel raise', 'fpi_total_i', 'fpi_total_d',
       'tibia vara proximal', 'tibia vara distal', 'rotula divergente',
       'rotula convergente', 'rotula ascendida', 'genu valgo', 'genu varo',
       'genu recurvatum', 'genu flexum', 'lunge', 'imc'],
      dtype='object')
In [32]:
columns = ['edad', 'sexo', 'altura', 'peso', 'num calzado', 'articulacion',
       'pace_walk', 'velocidad_walk', 'step rate_walk', 'stride length_walk', 'impact gs_walk', 'braking gs_walk',
       'pronation excursion_walk', 'contact ratio_walk', 'total force rate_walk', 'step length_walk',
       'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk',
       'stance excursion (mp->to)_walk', 'm1 hipermovil', 'thomas psoas',
       'thomas tfl', 'arco aplanado', 'arco elevado', 'arco transverso disminuido', 'index minus',
       'tfi', 'tfe', 'tti', 'tte', 'popliteo', 't_hintermann',
       'jack normal', 'jack no reconstruye', '2heel raise', 'fpi_total_d',
       'tibia vara proximal', 'rotula convergente', 'rotula ascendida', 'genu valgo', 'genu varo', 'lunge', 'imc']

# Configuración de PyCaret
clf1 = setup(data=df[columns], 
             target='articulacion', 
             session_id=123, 
             log_experiment=True, 
             experiment_name='clasificacion_articulacion',
             preprocess=True,  # Preprocesamiento automático
             train_size=0.8)  # Cómo dividir el conjunto de datos en PyCaret

# Comparar modelos automáticamente
best_model = compare_models()
best_model
  Description Value
0 Session id 123
1 Target articulacion
2 Target type Multiclass
3 Target mapping otro: 0, pie: 1, rodilla: 2, tobillo: 3
4 Original data shape (717, 43)
5 Transformed data shape (717, 43)
6 Transformed train set shape (573, 43)
7 Transformed test set shape (144, 43)
8 Numeric features 42
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Fold Generator StratifiedKFold
14 Fold Number 10
15 CPU Jobs -1
16 Use GPU False
17 Log Experiment MlflowLogger
18 Experiment Name clasificacion_articulacion
19 USI d2a4
  Model Accuracy AUC Recall Prec. F1 Kappa MCC TT (Sec)
rf Random Forest Classifier 0.4068 0.6288 0.4068 0.3997 0.3934 0.1920 0.1948 0.0210
gbc Gradient Boosting Classifier 0.3962 0.0000 0.3962 0.3978 0.3889 0.1817 0.1838 0.0790
et Extra Trees Classifier 0.3927 0.6237 0.3927 0.3797 0.3779 0.1735 0.1767 0.0140
xgboost Extreme Gradient Boosting 0.3806 0.6121 0.3806 0.3847 0.3749 0.1602 0.1618 0.0230
lightgbm Light Gradient Boosting Machine 0.3666 0.6113 0.3666 0.3719 0.3602 0.1414 0.1436 1.4270
lda Linear Discriminant Analysis 0.3647 0.0000 0.3647 0.3615 0.3534 0.1370 0.1394 0.0040
ridge Ridge Classifier 0.3543 0.0000 0.3543 0.3613 0.3409 0.1194 0.1224 0.0040
lr Logistic Regression 0.3490 0.0000 0.3490 0.3521 0.3387 0.1153 0.1170 0.0130
ada Ada Boost Classifier 0.3385 0.0000 0.3385 0.3281 0.3289 0.1054 0.1066 0.0100
dt Decision Tree Classifier 0.3351 0.5572 0.3351 0.3388 0.3335 0.1055 0.1061 0.0050
knn K Neighbors Classifier 0.3247 0.5597 0.3247 0.3126 0.3126 0.0839 0.0849 0.0040
nb Naive Bayes 0.2932 0.5469 0.2932 0.3054 0.2508 0.0452 0.0504 0.0040
svm SVM - Linear Kernel 0.2915 0.0000 0.2915 0.2508 0.1885 0.0385 0.0580 0.0060
dummy Dummy Classifier 0.2827 0.5000 0.2827 0.0799 0.1246 0.0000 0.0000 0.0040
qda Quadratic Discriminant Analysis 0.2686 0.0000 0.2686 0.3136 0.1904 0.0137 0.0157 0.0040
Out[32]:
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='sqrt',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_samples_leaf=1,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, n_estimators=100, n_jobs=-1,
                       oob_score=False, random_state=123, verbose=0,
                       warm_start=False)
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RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='sqrt',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_samples_leaf=1,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, n_estimators=100, n_jobs=-1,
                       oob_score=False, random_state=123, verbose=0,
                       warm_start=False)
In [33]:
columns = ['edad', 'sexo', 'imc', 'articulacion', 'pace_walk', 'stride length_walk', 'impact gs_walk', 
       'total force rate_walk', 'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk']

# Configuración de PyCaret
clf1 = setup(data=df[columns], 
             target='articulacion', 
             session_id=123, 
             log_experiment=True, 
             experiment_name='clasificacion_articulacion',
             preprocess=True,  # Preprocesamiento automático
             train_size=0.8)  # Cómo dividir el conjunto de datos en PyCaret

# Comparar modelos automáticamente
best_model = compare_models()
best_model
  Description Value
0 Session id 123
1 Target articulacion
2 Target type Multiclass
3 Target mapping otro: 0, pie: 1, rodilla: 2, tobillo: 3
4 Original data shape (717, 10)
5 Transformed data shape (717, 10)
6 Transformed train set shape (573, 10)
7 Transformed test set shape (144, 10)
8 Numeric features 9
9 Preprocess True
10 Imputation type simple
11 Numeric imputation mean
12 Categorical imputation mode
13 Fold Generator StratifiedKFold
14 Fold Number 10
15 CPU Jobs -1
16 Use GPU False
17 Log Experiment MlflowLogger
18 Experiment Name clasificacion_articulacion
19 USI 660e
  Model Accuracy AUC Recall Prec. F1 Kappa MCC TT (Sec)
et Extra Trees Classifier 0.3981 0.6172 0.3981 0.3863 0.3850 0.1814 0.1838 0.0150
gbc Gradient Boosting Classifier 0.3787 0.0000 0.3787 0.3762 0.3715 0.1584 0.1598 0.0370
lightgbm Light Gradient Boosting Machine 0.3701 0.6016 0.3701 0.3723 0.3656 0.1475 0.1490 1.1310
rf Random Forest Classifier 0.3682 0.6181 0.3682 0.3686 0.3596 0.1411 0.1433 0.0180
xgboost Extreme Gradient Boosting 0.3543 0.5920 0.3543 0.3539 0.3486 0.1247 0.1261 0.0150
lda Linear Discriminant Analysis 0.3403 0.0000 0.3403 0.3393 0.3225 0.0994 0.1027 0.0050
knn K Neighbors Classifier 0.3387 0.5902 0.3387 0.3286 0.3268 0.1010 0.1026 0.0060
ridge Ridge Classifier 0.3351 0.0000 0.3351 0.2961 0.2930 0.0833 0.0905 0.0040
dt Decision Tree Classifier 0.3315 0.5490 0.3315 0.3351 0.3267 0.0965 0.0977 0.0040
lr Logistic Regression 0.3300 0.0000 0.3300 0.3168 0.2971 0.0787 0.0838 0.0100
nb Naive Bayes 0.3175 0.5657 0.3175 0.3058 0.2711 0.0632 0.0744 0.0040
qda Quadratic Discriminant Analysis 0.3175 0.0000 0.3175 0.3133 0.2926 0.0686 0.0736 0.0030
ada Ada Boost Classifier 0.3158 0.0000 0.3158 0.3120 0.3103 0.0769 0.0776 0.0060
dummy Dummy Classifier 0.2827 0.5000 0.2827 0.0799 0.1246 0.0000 0.0000 0.0040
svm SVM - Linear Kernel 0.2776 0.0000 0.2776 0.1638 0.1624 0.0146 0.0256 0.0050
Out[33]:
ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,
                     criterion='gini', max_depth=None, max_features='sqrt',
                     max_leaf_nodes=None, max_samples=None,
                     min_impurity_decrease=0.0, min_samples_leaf=1,
                     min_samples_split=2, min_weight_fraction_leaf=0.0,
                     monotonic_cst=None, n_estimators=100, n_jobs=-1,
                     oob_score=False, random_state=123, verbose=0,
                     warm_start=False)
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ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,
                     criterion='gini', max_depth=None, max_features='sqrt',
                     max_leaf_nodes=None, max_samples=None,
                     min_impurity_decrease=0.0, min_samples_leaf=1,
                     min_samples_split=2, min_weight_fraction_leaf=0.0,
                     monotonic_cst=None, n_estimators=100, n_jobs=-1,
                     oob_score=False, random_state=123, verbose=0,
                     warm_start=False)
In [34]:
et = create_model('et', fold=10)
  Accuracy AUC Recall Prec. F1 Kappa MCC
Fold              
0 0.3621 0.5679 0.3621 0.3736 0.3514 0.1269 0.1297
1 0.4310 0.6115 0.4310 0.4144 0.4061 0.2232 0.2278
2 0.3103 0.5984 0.3103 0.2760 0.2886 0.0615 0.0630
3 0.4561 0.6305 0.4561 0.4282 0.4280 0.2585 0.2648
4 0.4912 0.6820 0.4912 0.4907 0.4889 0.3138 0.3147
5 0.3860 0.5837 0.3860 0.4096 0.3887 0.1681 0.1704
6 0.4386 0.6269 0.4386 0.4412 0.4370 0.2400 0.2412
7 0.3684 0.6317 0.3684 0.3341 0.3504 0.1393 0.1403
8 0.3684 0.6237 0.3684 0.3695 0.3673 0.1485 0.1490
9 0.3684 0.6154 0.3684 0.3257 0.3432 0.1342 0.1371
Mean 0.3981 0.6172 0.3981 0.3863 0.3850 0.1814 0.1838
Std 0.0515 0.0294 0.0515 0.0600 0.0543 0.0716 0.0720
In [35]:
plot_model(et)
In [36]:
plot_model(et, plot= 'confusion_matrix')

Árbol de Decisión¶

Modelo 1¶

In [37]:
params = {'max_depth': range(1, 11), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 5)}

best_model = train_evaluate_tree_model(X_train, y_train, X_test, y_test, params, cv_folds=10)
best_model

Train Accuracy: 0.4887
Test Accuracy: 0.4306
Best Parameters: {'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 2}
Out[37]:
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=4, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_samples_leaf=2,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, random_state=0, splitter='best')
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DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=4, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_samples_leaf=2,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, random_state=0, splitter='best')
In [39]:
evaluate_model(best_model, X_test, y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para DecisionTreeClassifier:
Accuracy: 0.4306
Precision: 0.3768
Recall: 0.4005
F1 Score: 0.3779

Visualización de la matriz de confusión exitosa
In [40]:
y_pred = best_model.predict(X_test)  # Etiquetas predichas por el modelo

plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.42      0.50      0.46        38
         pie       0.39      0.33      0.36        40
     rodilla       0.51      0.70      0.59        40
     tobillo       0.18      0.08      0.11        26

    accuracy                           0.43       144
   macro avg       0.38      0.40      0.38       144
weighted avg       0.40      0.43      0.40       144
In [41]:
from sklearn import tree

fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(best_model, 
                   feature_names=X_train.columns,  
                   filled=True)
In [42]:
plot_feature_importance(best_model, X_train.columns)

Modelo 2¶

In [43]:
columns = ['edad', 'sexo', 'imc', 'pace_walk', 'stride length_walk', 'impact gs_walk', 
           'total force rate_walk', 'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk',
          'step rate_walk', 'thomas tfl', 'tfi', 'index minus', 'arco aplanado']

params = {'max_depth': range(1, 11), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 5)}

best_model_2 = train_evaluate_tree_model(X_train[columns], y_train, X_test[columns], y_test, params, cv_folds=10)
best_model_2

Train Accuracy: 0.6684
Test Accuracy: 0.3542
Best Parameters: {'max_depth': 7, 'min_samples_leaf': 1, 'min_samples_split': 5}
Out[43]:
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=7, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_samples_leaf=1,
                       min_samples_split=5, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, random_state=0, splitter='best')
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DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=7, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_samples_leaf=1,
                       min_samples_split=5, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, random_state=0, splitter='best')
In [44]:
y_pred = best_model_2.predict(X_test[columns])  # Etiquetas predichas por el modelo

plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.33      0.18      0.24        38
         pie       0.41      0.42      0.42        40
     rodilla       0.41      0.60      0.48        40
     tobillo       0.13      0.12      0.12        26

    accuracy                           0.35       144
   macro avg       0.32      0.33      0.32       144
weighted avg       0.34      0.35      0.34       144
In [45]:
evaluate_model(best_model_2, X_test[columns], y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para DecisionTreeClassifier:
Accuracy: 0.3542
Precision: 0.3213
Recall: 0.3311
F1 Score: 0.3161

Visualización de la matriz de confusión exitosa

Modelo 3¶

In [46]:
columns = ['edad', 'sexo', 'altura', 'peso', 'num calzado', 'imc',
       'pace_walk', 'velocidad_walk', 'step rate_walk', 'stride length_walk', 'impact gs_walk', 'braking gs_walk',
       'pronation excursion_walk', 'contact ratio_walk', 'total force rate_walk', 'step length_walk',
       'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk',
       'stance excursion (mp->to)_walk', 'm1 hipermovil', 'thomas psoas',
       'thomas tfl', 'arco aplanado', 'arco elevado', 'arco transverso disminuido',
       'tfi', 'tfe', 'tti', 'tte', 'popliteo', 't_hintermann', 'index minus',
       'jack normal', 'jack no reconstruye', '2heel raise', 'fpi_total_d', 'tibia vara proximal', 
       'rotula convergente', 'rotula ascendida', 'genu valgo', 'genu varo', 'lunge']

params = {'max_depth': range(1, 11), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 5)}

best_model_3 = train_evaluate_tree_model(X_train[columns], y_train, X_test[columns], y_test, params, cv_folds=10)
best_model_3

Train Accuracy: 0.4887
Test Accuracy: 0.4306
Best Parameters: {'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 2}
Out[46]:
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=4, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_samples_leaf=2,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, random_state=0, splitter='best')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=4, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_samples_leaf=2,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, random_state=0, splitter='best')
In [47]:
evaluate_model(best_model_3, X_test[columns], y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para DecisionTreeClassifier:
Accuracy: 0.4306
Precision: 0.3768
Recall: 0.4005
F1 Score: 0.3779

Visualización de la matriz de confusión exitosa
In [48]:
y_pred = best_model_3.predict(X_test[columns])  # Etiquetas predichas por el modelo
plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.42      0.50      0.46        38
         pie       0.39      0.33      0.36        40
     rodilla       0.51      0.70      0.59        40
     tobillo       0.18      0.08      0.11        26

    accuracy                           0.43       144
   macro avg       0.38      0.40      0.38       144
weighted avg       0.40      0.43      0.40       144
In [49]:
fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(best_model_3, 
                   feature_names=columns,  
                   filled=True)
In [50]:
plot_feature_importance(best_model_3, X_train[columns].columns)

Modelo 4¶

In [51]:
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
           'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 
           'stride length_walk', 'edad', 'index minus','arco aplanado']

params = {'max_depth': range(1, 11), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 5)}

best_model_4 = train_evaluate_tree_model(X_train[columns], y_train, X_test[columns], y_test, params, cv_folds=10)
best_model_4

Train Accuracy: 0.4572
Test Accuracy: 0.4375
Best Parameters: {'max_depth': 3, 'min_samples_leaf': 1, 'min_samples_split': 2}
Out[51]:
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=3, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_samples_leaf=1,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, random_state=0, splitter='best')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=3, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_samples_leaf=1,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, random_state=0, splitter='best')
In [52]:
evaluate_model(best_model_4, X_test[columns], y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para DecisionTreeClassifier:
Accuracy: 0.4375
Precision: 0.3388
Recall: 0.4000
F1 Score: 0.3663

Visualización de la matriz de confusión exitosa
In [53]:
y_pred = best_model_4.predict(X_test[columns])  # Etiquetas predichas por el modelo
plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.40      0.50      0.44        38
         pie       0.43      0.45      0.44        40
     rodilla       0.53      0.65      0.58        40
     tobillo       0.00      0.00      0.00        26

    accuracy                           0.44       144
   macro avg       0.34      0.40      0.37       144
weighted avg       0.37      0.44      0.40       144
In [54]:
fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(best_model_4, 
                   feature_names=columns,  
                   filled=True)
In [55]:
plot_feature_importance(best_model_4, X_train[columns].columns)

Evaluación de Modelos¶

1. Modelo 1 y Modelo 3:

  • Train Accuracy: 0.4887
  • Test Accuracy: 0.4306
  • Precision: 0.3768 (macro avg), 0.40 (weighted avg)
  • Recall: 0.4005 (macro avg), 0.43 (weighted avg)

  • F1 Score: 0.3779 (macro avg), 0.40 (weighted avg)

    Ambos modelos son idénticos en términos de parámetros y métricas de desempeño, mostrando una precisión y recall moderados en comparación con los otros modelos. Son consistentes en términos de desempeño tanto en entrenamiento como en prueba.

2. Modelo 2:

  • Train Accuracy: 0.6684 (más alto entre los modelos)
  • Test Accuracy: 0.3542 (el más bajo)
  • Precision: 0.3213 (macro avg), 0.34 (weighted avg)
  • Recall: 0.3311 (macro avg), 0.35 (weighted avg)

  • F1 Score: 0.3161 (macro avg), 0.34 (weighted avg)

    Aunque este modelo tiene la mayor precisión en el conjunto de entrenamiento, su rendimiento cae significativamente en el conjunto de prueba, indicando un posible sobreajuste.

3. Modelo 4:

  • Train Accuracy: 0.4572
  • Test Accuracy: 0.4375 (el más alto entre los modelos)
  • Precision: 0.3388 (macro avg), 0.37 (weighted avg)
  • Recall: 0.4000 (macro avg), 0.44 (weighted avg)

  • F1 Score: 0.3663 (macro avg), 0.40 (weighted avg)

    Este modelo tiene la mayor precisión en el conjunto de prueba y muestra una mejora leve sobre los modelos 1 y 3, aunque hay un desequilibrio significativo en la precisión y recall entre las categorías, destacando un desempeño pobre en la categoría 'tobillo' donde no se identificó correctamente ningún caso.

Conclusión¶

El Modelo 4 parece ser el más prometedor en términos de precisión general de prueba y manejo del desequilibrio entre las categorías de la variable objetivo. Aunque la precisión macro promedio es más baja, muestra la precisión más alta y consistente en el conjunto de prueba, lo que sugiere que es más robusto y generaliza mejor sobre datos no vistos en comparación con los otros modelos.

Razones para Elegir el Modelo 4¶

  • Selección de Características: El Modelo 4 utiliza un conjunto de características que incluye algunas de las más influyentes según los análisis de importancia de características (como total force rate_walk, pace_walk, stride length_walk, y impact gs_walk). Esta selección parece proporcionar un buen equilibrio entre la cantidad de información y el rendimiento del modelo, evitando posiblemente el sobreajuste que podríamos ver en el Modelo 1 que usa todas las características.

  • Rendimiento General: A pesar de que el Modelo 4 no tiene la mayor precisión en los entrenamientos (como el Modelo 2), muestra la mejor precisión de prueba, lo que indica que generaliza mejor en datos no vistos.

  • Complejidad del Modelo: El Modelo 4, con una selección más enfocada de características, probablemente sea menos complejo y más interpretable que el Modelo 1, que usa todas las características.

  • Balanced Feature Set: La elección de las características en el Modelo 4 parece estar bien equilibrada para capturar tanto los aspectos biomecánicos (ej., stride length_walk, total force rate_walk) como los factores de riesgo o condicionantes personales (ej., edad, genu valgo).

Random Forest¶

Modelo 1 - Sin Selección de Características¶

In [57]:
param_grid = {
    'max_depth': range(1, 15),
    'min_samples_split': [2, 5],
    'min_samples_leaf': [1, 2],
    'max_features': ['sqrt', 'log2']
}

rf = train_random_forest(X_train, y_train, X_train.columns, param_grid, n_jobs=1, cv=3)

Fitting 3 folds for each of 112 candidates, totalling 336 fits
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
Best mean cross-validation score: 0.366
Best parameters: {'max_depth': 6, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2}
In [58]:
evaluate_model(rf, X_test, y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para RandomForestClassifier:
Accuracy: 0.3819
Precision: 0.3526
Recall: 0.3557
F1 Score: 0.3457

Visualización de la matriz de confusión exitosa
In [59]:
y_pred = rf.predict(X_test)  # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.48      0.42      0.45        38
         pie       0.34      0.45      0.39        40
     rodilla       0.40      0.47      0.44        40
     tobillo       0.18      0.08      0.11        26

    accuracy                           0.38       144
   macro avg       0.35      0.36      0.35       144
weighted avg       0.37      0.38      0.37       144
In [60]:
plot_multiclass_roc(rf, X_test, y_test)
In [61]:
plot_feature_importance(rf, X_train.columns, figsize=(10,12))

Modelo 2 - Con Selección de Características¶

In [87]:
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
           'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 'contact ratio_walk',
           'stride length_walk', 'edad', 'index minus','arco aplanado', 'stance excursion (fs->mp)_walk']

param_grid = {
    'max_depth': range(1, 15),
    'min_samples_split': [2, 5],
    'min_samples_leaf': [1, 2],
    'max_features': ['sqrt', 'log2']
}

rf2 = train_random_forest(X_train, y_train, columns, param_grid, n_jobs=1, cv=3)

Fitting 3 folds for each of 112 candidates, totalling 336 fits
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
Best mean cross-validation score: 0.379
Best parameters: {'max_depth': 8, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5}
In [63]:
evaluate_model(rf2, X_test[columns], y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para RandomForestClassifier:
Accuracy: 0.4028
Precision: 0.3857
Recall: 0.3812
F1 Score: 0.3757

Visualización de la matriz de confusión exitosa
In [64]:
y_pred = rf2.predict(X_test[columns])  # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.42      0.42      0.42        38
         pie       0.36      0.40      0.38        40
     rodilla       0.46      0.55      0.50        40
     tobillo       0.31      0.15      0.21        26

    accuracy                           0.40       144
   macro avg       0.39      0.38      0.38       144
weighted avg       0.39      0.40      0.39       144
In [88]:
# Evaluación del modelo
train_accuracy = rf2.score(X_train[columns], y_train)
test_accuracy = rf2.score(X_test[columns], y_test)

print(f"Train Accuracy: {train_accuracy:.4f}")
print(f"Test Accuracy: {test_accuracy:.4f}")

Train Accuracy: 0.8517
Test Accuracy: 0.4028
In [65]:
plot_multiclass_roc(rf2, X_test[columns], y_test)
In [66]:
plot_feature_importance(rf2, X_train[columns].columns)

Observaciones¶

  • La categoría "rodilla" muestra el mejor recall, lo que indica que el modelo es relativamente más eficaz en detectar esta categoría.
  • Las categorías "otro" y "pie" tienen un rendimiento moderado.
  • La categoría "tobillo" muestra un rendimiento bajo, especialmente en términos de recall, lo que sugiere que el modelo lucha por identificar correctamente esta categoría.
In [68]:
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
           'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 'contact ratio_walk',
           'stride length_walk', 'edad', 'index minus','arco aplanado', 'stance excursion (fs->mp)_walk']

param_grid = {
    'max_depth': range(1, 15),
    'min_samples_split': [2, 5],
    'min_samples_leaf': [1, 2],
    'max_features': ['sqrt', 'log2']
}

rf2 = train_random_forest_2(X_train, y_train, columns, param_grid, cv=5, n_jobs=1)

Fitting 5 folds for each of 112 candidates, totalling 560 fits
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
Best mean cross-validation score: 0.390
Best parameters: {'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5}
In [69]:
evaluate_model(rf2, X_test[columns], y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para RandomForestClassifier:
Accuracy: 0.4306
Precision: 0.4287
Recall: 0.4338
F1 Score: 0.4305

Visualización de la matriz de confusión exitosa

Modelo 2 con RandomisedSearchCV¶

In [89]:
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
           'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 'contact ratio_walk',
           'stride length_walk', 'edad', 'index minus','arco aplanado', 'stance excursion (fs->mp)_walk']

params = {
    'max_depth': range(1, 15),
    'min_samples_split': [2, 5],
    'min_samples_leaf': [1, 2],
    'max_features': ['sqrt', 'log2']
}

rf3 = train_random_forest_randomized(X_train, y_train, columns, params, cv=5,  n_jobs=1)

Fitting 5 folds for each of 100 candidates, totalling 500 fits
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.0s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.1s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.0s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time=   0.1s
Best mean cross-validation score: 0.390
Best parameters: {'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 5}
In [72]:
evaluate_model(rf3, X_test[columns], y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para RandomForestClassifier:
Accuracy: 0.4306
Precision: 0.4287
Recall: 0.4338
F1 Score: 0.4305

Visualización de la matriz de confusión exitosa
In [73]:
y_pred = rf3.predict(X_test[columns])  # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.50      0.47      0.49        38
         pie       0.35      0.33      0.34        40
     rodilla       0.46      0.47      0.47        40
     tobillo       0.40      0.46      0.43        26

    accuracy                           0.43       144
   macro avg       0.43      0.43      0.43       144
weighted avg       0.43      0.43      0.43       144
In [90]:
# Evaluación del modelo
train_accuracy = rf3.score(X_train[columns], y_train)
test_accuracy = rf3.score(X_test[columns], y_test)

print(f"Train Accuracy: {train_accuracy:.4f}")
print(f"Test Accuracy: {test_accuracy:.4f}")

Train Accuracy: 0.6719
Test Accuracy: 0.4306
In [74]:
plot_multiclass_roc(rf3, X_test[columns], y_test)

Bagging Classifier¶

In [76]:
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
           'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 'contact ratio_walk',
           'stride length_walk', 'edad', 'index minus','arco aplanado', 'stance excursion (fs->mp)_walk']

# Configurar el RandomForestClassifier como el estimador base
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42, class_weight='balanced')

# Configurar el BaggingClassifier utilizando el RandomForest como estimador
bagging_model = BaggingClassifier(
    estimator=rf_classifier,
    n_estimators=10,
    random_state=42
)

# Configuración para GridSearchCV
# Crear un GridSearchCV para optimizar max_depth del RandomForest
param_grid_bagging = {'estimator__max_depth': range(1, 15)}
grid_bagging = GridSearchCV(bagging_model, param_grid=param_grid_bagging, scoring='balanced_accuracy', cv=10, verbose=2)
grid_bagging.fit(X_train[columns], y_train)

# Imprimir los resultados
print("Best mean cross-validation score: {:.3f}".format(grid_bagging.best_score_))
print("Best parameters: {}".format(grid_bagging.best_params_))

# Extracción de resultados para visualización
scores = grid_bagging.cv_results_['mean_test_score']
max_depths = range(1, 15)

# Gráfica de los resultados
plt.figure(figsize=(8, 4))
plt.plot(max_depths, scores, '-o')
plt.xlabel('Max Depth of Base Estimator')
plt.ylabel('Balanced Accuracy')
plt.title('Performance vs Max Depth in Bagging')
plt.grid(True)
plt.show()

Fitting 10 folds for each of 14 candidates, totalling 140 fits
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=1; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.5s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=2; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=3; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=4; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=5; total time=   0.4s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=6; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=7; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=8; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END .............................estimator__max_depth=9; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=10; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.6s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=11; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.6s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=12; total time=   0.5s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.6s
[CV] END ............................estimator__max_depth=13; total time=   0.5s
[CV] END ............................estimator__max_depth=14; total time=   0.5s
[CV] END ............................estimator__max_depth=14; total time=   0.5s
[CV] END ............................estimator__max_depth=14; total time=   0.6s
[CV] END ............................estimator__max_depth=14; total time=   0.6s
[CV] END ............................estimator__max_depth=14; total time=   0.6s
[CV] END ............................estimator__max_depth=14; total time=   0.5s
[CV] END ............................estimator__max_depth=14; total time=   0.5s
[CV] END ............................estimator__max_depth=14; total time=   0.5s
[CV] END ............................estimator__max_depth=14; total time=   0.5s
[CV] END ............................estimator__max_depth=14; total time=   0.5s
Best mean cross-validation score: 0.377
Best parameters: {'estimator__max_depth': 5}
In [77]:
best_bagging_model = grid_bagging.best_estimator_

print("Test Accuracy: {:.3f}".format(best_bagging_model.score(X_test[columns], y_test)))
print("Train: ",best_bagging_model .score(X_train[columns],y_train))
print("Test: ",best_bagging_model .score(X_test[columns],y_test))

Test Accuracy: 0.451
Train:  0.743455497382199
Test:  0.4513888888888889
In [78]:
evaluate_model(best_bagging_model, X_test[columns], y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para BaggingClassifier:
Accuracy: 0.4514
Precision: 0.4502
Recall: 0.4526
F1 Score: 0.4499

Visualización de la matriz de confusión exitosa
In [79]:
y_pred = best_bagging_model.predict(X_test[columns])  # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.53      0.47      0.50        38
         pie       0.42      0.38      0.39        40
     rodilla       0.45      0.50      0.48        40
     tobillo       0.40      0.46      0.43        26

    accuracy                           0.45       144
   macro avg       0.45      0.45      0.45       144
weighted avg       0.45      0.45      0.45       144
In [80]:
plot_multiclass_roc(best_bagging_model, X_test[columns], y_test)
In [81]:
plot_feature_importances_bagging(best_bagging_model, columns)

Gradient Boosting Classifier¶

In [82]:
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV

# Configuración de la búsqueda en malla para GradientBoostingClassifier
param_grid_gbc = {
    #'n_estimators': [100, 200, 300],
    'n_estimators': [100, 200],
    'max_depth': range(3, 10),
    'learning_rate': [0.01, 0.1, 0.2]
}

# Crear el modelo de GradientBoostingClassifier
gbc = GradientBoostingClassifier(random_state=42)
grid_gbc = GridSearchCV(estimator=gbc, param_grid=param_grid_gbc, cv=10, scoring='balanced_accuracy', verbose=2)
grid_gbc.fit(X_train[columns], y_train)

# Imprimir los mejores resultados de la validación cruzada
print("Best mean cross-validation score: {:.3f}".format(grid_gbc.best_score_))
print("Best parameters: {}".format(grid_gbc.best_params_))

Fitting 10 folds for each of 42 candidates, totalling 420 fits
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.4s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.4s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.4s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.4s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time=   2.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.9s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.9s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time=   2.8s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.7s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.4s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.7s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   2.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   2.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time=   1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.6s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.6s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.6s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.6s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time=   2.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time=   0.7s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time=   0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   1.0s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time=   1.2s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time=   0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.6s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time=   1.5s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   0.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.8s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   2.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time=   1.9s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time=   1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time=   2.1s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.4s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time=   1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.2s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time=   2.3s
Best mean cross-validation score: 0.375
Best parameters: {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 200}
In [83]:
# Ajustar el modelo óptimo
best_gbc = GradientBoostingClassifier(random_state=42, max_depth=grid_gbc.best_params_['max_depth'],
                                      learning_rate=grid_gbc.best_params_['learning_rate'],
                                      n_estimators=grid_gbc.best_params_['n_estimators'])
gbc = best_gbc.fit(X_train[columns], y_train)

# Evaluación del modelo
print("Train accuracy: {:.3f}".format(best_gbc.score(X_train[columns], y_train)))
print("Test accuracy: {:.3f}".format(best_gbc.score(X_test[columns], y_test)))

Train accuracy: 0.918
Test accuracy: 0.319
In [84]:
evaluate_model(gbc, X_test[columns], y_test)

Predicciones realizadas con éxito
Conversión a numpy arrays exitosa
Cálculo de métricas exitoso
Resultados para GradientBoostingClassifier:
Accuracy: 0.3194
Precision: 0.3124
Recall: 0.3093
F1 Score: 0.3082

Visualización de la matriz de confusión exitosa
In [85]:
y_pred = gbc.predict(X_test[columns])  # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)

Classification Report:

              precision    recall  f1-score   support

        otro       0.35      0.39      0.37        38
         pie       0.29      0.28      0.28        40
     rodilla       0.33      0.38      0.35        40
     tobillo       0.28      0.19      0.23        26

    accuracy                           0.32       144
   macro avg       0.31      0.31      0.31       144
weighted avg       0.32      0.32      0.32       144
In [86]:
plot_multiclass_roc(gbc, X_test[columns], y_test)